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Data scientist working from home In today's data-driven world, the role of a data scientist has become essential across various industries. As organizations increasingly leverage data to make informed decisions, data scientists play a pivotal role in deciphering complex data sets and deriving actionable insights. This blog post aims to explore the essential roles and responsibilities of a data scientist in your career journey in data, providing a comprehensive overview of what you can expect if you choose this exciting path. Understanding the Role of a Data Scientist At its core, the role of a data scientist involves analyzing and interpreting complex data. A data scientist utilizes a combination of statistics, mathematics, programming, and domain knowledge to solve data-related problems. They uncover patterns, make predictions, and provide recommendations that help organizations achieve their strategic goals. Data scientists are not just number crunchers; they are storytellers who can communicate their findings to both technical and non-technical stakeholders. By presenting their insights in a clear and compelling manner, data scientists can influence decisions that shape the future of the organization. Key Responsibilities of a Data Scientist Data Collection and Preparation One of the primary responsibilities of a data scientist is to collect and prepare data for analysis. This involves identifying the right data sources, cleaning the data to eliminate inconsistencies or errors, and transforming it into a format suitable for analysis. Data preparation is crucial, as the quality of the data directly impacts the reliability of the insights derived from it. This phase may also include handling missing data, normalizing data sets, and enriching data with additional information. Exploratory Data Analysis (EDA) Example of Exploratory Data Analysis Project Exploratory Data Analysis is a vital step in the data science process that allows data scientists to understand the data better. Through visualizations and statistical methods, data scientists investigate data patterns and relationships that could inform future analysis. This phase helps identify trends, outliers, and anomalies, enabling data scientists to refine their analytical strategies and hypotheses. EDA is essential for building a strong foundation for the modeling phase that follows. Model Building and Evaluation Once the data is prepared and understood, the next responsibility is to build predictive models. Data scientists use various algorithms and techniques—from regression and classification to clustering and neural networks—to create models that can predict outcomes based on historical data. Model evaluation is crucial to ensure the performance and accuracy of these predictive models. Data scientists utilize metrics such as precision, recall, and F1 score to assess model effectiveness and iterate until they achieve satisfactory results. Deployment and Maintenance Creating a model is just the beginning. Once a model has been built, it needs to be deployed into a production environment where it can be used for real-time predictions. Data scientists work closely with data engineers and IT teams to ensure models are integrated smoothly into existing systems. Additionally, continuous monitoring and maintenance are required. Data scientists must ensure models remain relevant and accurate as new data becomes available. This may involve retraining the model or updating it with new techniques as advancements in technology and methodologies emerge. Communication and Collaboration A crucial aspect of a data scientist's role is communication. Data scientists must present their findings in a way that is understandable to non-technical stakeholders. This includes crafting reports, creating visualizations, and leading discussions to explain complex findings. Collaboration with other team members is also vital. Data scientists often work alongside data engineers, analysts, and business leaders to drive projects forward, ensuring a multidisciplinary approach to solving data challenges. Skills Required for Success Skillset Required for a Data Scientist To excel in this role, a variety of skills are necessary: Statistical Analysis : Proficiency in statistics and the ability to derive insights from data is fundamental. Programming : Familiarity with programming languages such as Python or R is essential for data manipulation and model development. Machine Learning : Understanding machine learning algorithms and frameworks is critical for building predictive models. Data Visualization : The ability to create informative visualizations that clearly convey insights is important for effective communication. Problem-Solving : Strong analytical and critical thinking skills are essential to tackle complex data problems and make data-driven decisions. The Evolving Landscape of Data Science As technology evolves, so does the role of the data scientist. New tools, frameworks, and methodologies are continually emerging, challenging data scientists to stay updated and adapt to changes. Furthermore, the demand for data scientists is growing, leading to increased competition. Continuous learning and upskilling are vital for those aiming to maintain their relevance in the field. Conclusion The journey of a data scientist is dynamic and rewarding, filled with opportunities to solve real-world problems through data. By understanding the essential roles and responsibilities in this career, aspiring data professionals can better prepare for a future in data science. Navigating this path requires a mix of technical, analytical, and interpersonal skills, as well as a commitment to continuous learning. As the demand for data-driven insights continues to rise, the role of the data scientist will remain indispensable, driving innovation and enabling informed decision-making across various sectors. Embarking on a career journey in data science is not just about mastering tools and techniques; it's about transforming data into meaningful insights that can impact organizations and society as a whole. By honing the skills mentioned and embracing collaboration and communication, you can thrive in this exciting and evolving field.

Data Analyst at work In today's data-driven world, the role of a data analyst has become increasingly vital across various industries. As organizations continue to rely on data to make informed decisions, understanding the roles and responsibilities of a data analyst becomes crucial for aspiring professionals in the field. Whether you are a budding data scientist, an experienced data engineer, or someone looking to pursue a career in data, grasping the core competencies of a data analyst will help you navigate your career journey effectively. Understanding the Role of a Data Analyst A data analyst is primarily tasked with interpreting complex datasets to provide actionable insights. This involves gathering, processing, and analyzing data to identify trends, develop reports, and inform strategic decisions. By leveraging statistical tools and software, data analysts transform raw data into understandable formats, which play a significant role in shaping key business strategies. The role requires a combination of analytical and technical skills, as well as a keen understanding of the business domain. As a data analyst, you will engage with multiple stakeholders, necessitating communication skills that facilitate collaboration across departments. Understanding the role of a data analyst Data Collection and Management One of the primary responsibilities of a data analyst is data collection. You will frequently gather data from various sources, including databases, surveys, and external datasets. This task requires an understanding of data integrity and adherence to ethical standards. Once the data is collected, effective data management practices must be employed. This includes data cleaning—removing inaccuracies, filling in missing values, and ensuring the data adheres to a consistent format. A robust data management strategy not only improves the quality of your analysis but also helps in making sound conclusions that drive business actions. Data Analysis and Interpretation After cleaning the data, the next responsibility is to analyze it. Here, you will apply various statistical methods to identify patterns, correlations, and trends. This phase often employs tools like SQL, R, or Python, depending on the complexity of your analysis. Interpreting the analyzed data is equally important. This is where your analytical skills truly shine as you distill complex numerical insights into actionable recommendations for stakeholders. Effective visualization techniques can also be crucial in this step; using tools like Tableau or Power BI enhances the understanding of your findings. Reporting and Presenting Insights Good data analysis does not stop at number crunching; it also involves effective communication of your findings. A data analyst must prepare comprehensive reports that summarize insights and suggest actionable next steps. These reports should be tailored to the specific audience they address—executives may require high-level summaries, while technical teams may benefit from detailed data breakdowns. Presenting insights in a clear, compelling manner, whether through written reports, visual dashboards, or presentations, will ensure that your findings have the desired impact. Remember that you are not merely presenting data; you are telling a story that contributes to informed decision-making. Example of a data report Collaborating with Stakeholders Collaboration is another cornerstone of a data analyst's role. You will often work closely with data scientists, financial analysts, and even marketing professionals to ensure that your findings align with overarching business strategies. This can involve regular meetings and discussions to gather input and feedback. In many instances, you may also interface with IT or data engineering teams to establish data collection pipelines or improve data storage practices. Being able to understand and navigate these interdependencies is vital for successful data analysis and subsequent implementation of insights. Data collaboration session Continuous Learning and Adaptation The realm of data analytics is ever-evolving, with new tools, technologies, and methodologies emerging frequently. As a data analyst, one of your responsibilities is to stay current with industry trends and advancements. This could include pursuing certifications, attending workshops, or enrolling in online courses relevant to your field. A commitment to continuous learning not only enhances your skill set but also positions you as a valuable asset to your organization. In a fast-paced environment, adaptability is key to leveraging new data techniques effectively. The Importance of Data Quality Assessment Another vital responsibility you may encounter is data quality assessment. This involves evaluating the accuracy and reliability of the data you are working with. Poor data quality can lead to misleading conclusions and potentially harmful business decisions. Regular checks should be put in place to confirm that the data meets the necessary standards. Employing software tools that specialize in data validation can assist you in ensuring that the datasets you utilize are trustworthy. Conclusion As you navigate your career journey in data, understanding the roles and responsibilities of a data analyst will equip you with the necessary tools to succeed. From data collection and analysis to interpretation and collaboration, each responsibility plays a pivotal role in the overall data life cycle. By honing your skills in these areas and embracing a mindset of continuous learning, you will not only become an effective data analyst but also elevate your career prospects in the ever-expanding field of data. Embrace these responsibilities as integral steps on your path to success, and remember that every piece of data tells a story waiting to be uncovered. By dedicating yourself to these responsibilities, you enhance not only your own career in data but also contribute meaningfully to your organization’s success.

Dynamic 365 Sales & Marketing Dashboard Dynamic 365 Case Study - Context An early startup business agility consultancy seeks to enter the Lean-Agile Certification market in order to Grow its revenue by 10x Expand its customer based and customer reach - both current and prospective customer Create a global community of certified Lean-Agile practitioners The Lean-Agile Certification program is divided into four work streams: Strategy - Focus on creating the program strategy, strategic priorities, and strategic roadmap Product - turning strategy, strategic priorities into products and ensuring the end-to-end product development lifecycle Sales and Marketing - responsible for planning, developing, and executing strategies to promote and sell a company's products or services Service Delivery : responsible for delivering of certification program and ensure customer satisfaction Dynamic 365 Case Study - Your Role You are a Dynamics 365 Consultant, and your primary responsibility is to support the sales and marketing team in implementing a Dynamics 365 solution for a Certification Program. The objective is to design a streamlined, automated, and efficient system that enhances customer experience, improves sales and marketing effectiveness, and provides real-time insights into program performance Dynamic 365 Case Study - Interview Task This will be a 60-minute interview centered around the Dynamic 365 Case Study - 20-minute presentation, 20-minute Q&A session and 20-minute feedback session. You will need to prepare a 20-minute presentation outlining your proposed solution. Your presentation should be designed for a sales and marketing audience with little or no experience in Dynamics 365, focusing on how the solution can drive business value while keeping costs low. Your presentation should cover: Analysis Requirement Gathering Process Flow Stakeholder Management Business Value Dynamic 365 Case Study - Summary Provide clear explanations and justifications for your choices Highlight how they meet the objective of implementing a Dynamics 365 solution for a Certification Program Please ensure that a suitable presentation has been prepared and email the presentation to contact@itydata.com 24 hours in advance of the interview Extra - Additional Guide and Things to Think About Analysis Conduct a thorough analysis of the existing sales and marketing processes related to certification programs. Identify pain points, inefficiencies, and areas requiring automation. Benchmark industry best practices for managing certification programs. Assess existing CRM and ERP integrations to ensure seamless data flow. Requirement Gathering Engage with key stakeholders (Sales Managers, Marketing Heads, Certification Coordinators, IT Team, and Customers) to understand their needs. Define functional and non-functional requirements for the new solution. Identify necessary integrations (e.g., Learning Management System (LMS), payment gateways, and customer portals). Document user stories and acceptance criteria to ensure alignment with business goals. Process Flow Design an end-to-end process flow for managing the certification program using Dynamics 365 Sales and Marketing modules. Automate lead generation and nurturing through customer engagement journeys. Implement customer self-service portals for registration, payment, and certification tracking. Enable workflow automation for certification issuance, renewals, and reminders. Ensure data synchronization between sales, marketing, and finance teams. Stakeholder Management Define roles and responsibilities for different teams using Dynamics 365. Develop a stakeholder engagement plan to ensure buy-in and adoption. Conduct regular meetings, demonstrations, and feedback sessions. Address change management challenges and provide training to end users. Business Value Improve lead-to-conversion rates through targeted marketing campaigns. Enhance customer experience with self-service capabilities and real-time updates. Increase operational efficiency by automating manual tasks and reducing errors. Provide actionable insights through real-time dashboards and analytics. Drive revenue growth by optimizing the certification program’s sales and marketing efforts.

Your First Data Projects Embarking on your first data project can be both thrilling and intimidating. Whether you're a student or a professional eager to expand your skillset, there's a world of information waiting for you to explore. In this blog post, we'll detail the steps to initiate your first data project, the tools you'll need, and the fascinating insights you can uncover along the way. Let’s jump in! Understanding Your Data Project Goals The first step is to clearly define what you want to achieve with your data project. Ask yourself questions like: What are the specific trends you want to analyze? Are you aiming to make predictions or understand relationships between certain variables? Setting defined objectives will steer your project in the right direction. Think of this stage like planning a road trip. You wouldn't just drive aimlessly without a destination. Having a clear purpose will keep you focused and motivated. Choosing the Right Dataset Once your goals are set, it’s time to select a dataset that suits your objectives. Many websites provide free datasets. Resources like Kaggle , the UCI Machine Learning Repository , and various government data portals are treasure troves for aspiring data analysts. When you're browsing, consider the following factors about the dataset: Relevance : Ensure that it aligns with your project goals. For instance, if you're looking to analyze climate change effects, a dataset on global temperatures published by NASA may be ideal. Size : A larger dataset can provide more insights but may require more processing power. Conversely, smaller datasets can be easier to work with to start. For example, a dataset containing 10,000 entries might yield more diverse insights than one with only 500. Completeness : Check if the dataset has missing values. For example, missing data for 10% of crucial variables could skew your analysis. Choosing the right dataset is like selecting fresh produce at a market—the quality directly influences the final outcome. Setting Up Your Environment With your dataset in hand, it's time to set up your working environment. Depending on your comfort level, you might choose from the following tools: Python : Great for data manipulation using libraries like Pandas, NumPy, and Matplotlib. For example, a 2021 survey indicated that over 55% of data scientists prefer Python due to its versatility. R : Excellent for statistical analysis and data visualization. Excel : Suitable for simpler data analyses, especially if you're just starting out. Google Sheets : Suitable for simpler data analyses and it is free. Whichever tool you select, make sure you have it installed and properly configured. Having the right tools is like having the proper ingredients before starting to cook a new dish! Data Cleaning: The Crucial First Step No dataset is flawless. Expect that data cleaning might take up about 70-80% of your project time. But don't underestimate its significance. Here are some common tasks to focus on: Handling missing values : You might replace missing entries with mean values, or if significant, consider removing the data points altogether. For instance, if 15% of a dataset is missing, filling in averages may obscure trends. Removing duplicates : Duplicate entries can distort your findings. You might find that a dataset has the same customer data listed multiple times, which can skew insights. Standardizing formats : Ensure consistency in how data is presented. If your dataset has dates in various formats (e.g., MM/DD/YYYY vs. DD/MM/YYYY), standardizing will help avoid confusion during analysis. Properly cleaned data forms the bedrock for insightful analysis, just like preparing your ingredients is essential for a delicious meal. Exploring Your Data After cleaning your data, it's time to explore! Use descriptive statistics and visualization tools to gain insights. Tools like Python's Seaborn or R's ggplot2 help create visual representations to identify patterns and trends. Look for: Outliers : Certain data points may stand out distinctly from the rest. For example, an unusually high sales figure in one region could spark further investigation. Noteworthy correlations : Are there relationships between variables? For instance, you may find that as advertising spend increases, sales also rise, indicating a correlation. This exploration phase allows your curiosity to shine, leading to rich storytelling from your data. The more you investigate, the more compelling narratives you can reveal! Analyzing the Data Now it’s time for the fun part: analyzing your data to draw conclusions. Depending on your goals, you might use a range of statistical techniques. This could range from simple techniques like linear regression to more advanced machine learning algorithms. For instance, if you wanted to predict house prices based on features like location and size, you might apply a linear regression model with machine learning tools like Scikit-learn. Throughout this process, document your methodologies and findings carefully. This practice not only keeps your analysis organized but also allows others to benefit from your discoveries. Analyzing data is akin to conducting an experiment; meticulous attention ensures reliable results. Communicating Your Findings Once you've extracted valuable insights, it’s essential to convey your findings effectively. Whether you're drafting a report, delivery an engaging presentation, or creating a visual dashboard, keep in mind these key components: Visuals : Use clear charts and graphs that depict your findings. Data visualizations can transform complex data into understandable stories. Research shows that visuals can enhance retention of information by up to 65%. Context : Explain why your insights matter. For instance, if you find a significant drop in sales during a specific month, discuss potential causes and implications for future strategies. Actionable recommendations : Based on your analysis, suggest concrete steps. For instance, if your data reveals declining customer engagement, recommend targeted marketing campaigns to recapture that audience. The objective is to weave a narrative with your data. Successful communication is like telling an engaging story—your audience will be more likely to act on your insights! Reflecting on the Process Once your project is complete, take a moment to reflect. Consider challenges you faced and techniques you learned. Did your findings align with your initial predictions? Evaluating your experience is vital for future projects. Every data project offers valuable lessons. Celebrate your achievements and learn from mistakes. Each experience adds to your journey toward becoming a skilled data professional! Your Next Steps Starting your first data project can open a world of learning and discovery. From setting clear goals and selecting the right dataset to exploring, analyzing, and communicating your findings, each step matters. So, grab your dataset and embark on your data journey today! Remember, the secret to unraveling incredible findings lies in your curiosity and creativity. Happy exploring!

Work Experience Program - Data Analyst Background - Work Experience ITyDATA envisions a world where individuals and organizations unlock the transformative power of agility, data, technology, and career growth. To support those looking to gain hands-on experience, ITyDATA offers an intensive, self-paced work experience program that bridges the gap between knowledge and real-world application. Participants tackle real business challenges, enhancing their skills and expertise through practical, industry-relevant projects Problem ITyDATA’s work experience program has become its flagship offering, empowering over 200 individuals to gain practical experience and transition into both technical and non-technical roles. Recognising its success, the senior management team is actively exploring ways to enhance its value, engagement, and overall impact on career transitions, career changes, and professional growth. They believe there is significant potential to further strengthen the program as a powerful enabler for career advancement. Task As a Management Consultant Trainee, the aim of my project was analysing both internal and external sources of dissatisfaction for the work experience program services as part of the STATIK (System Thinking Approach to Introducing Kanban) initiative. Method Three 90-minute workshops were conducted to identify sources of dissatisfaction and understand customer needs. The first workshop focused on external dissatisfaction by gathering feedback from customers. The second workshop addressed internal challenges by identifying sources of dissatisfaction within the service delivery team. An additional 90-minute session was held to analyze customer profiles and their purpose for joining the work experience program. All findings were presented to the senior management team using Miro Result Figure 1 shows external sources of dissatisfaction for a segment of the customers Figure 1 - External Sources of Dissatisfaction - Customers Figure 2 shows internal sources of dissatisfaction for the Work Experience Program Delivery Team Figure 2 - Internal Sources of Dissatisfaction for the Work Experience Program Delivery Team Figure 3 shows customer profiles and their purpose Figure 3 - Customer Profiles and their Purposes Outcome The Work Experience Program offers a valuable opportunity for individuals seeking to establish their careers. By providing hands-on experience, recognized references, and access to the job market, the program equips participants with essential tools for a successful career transition. For senior management, this initiative delivered critical insights from both customers and employees, helping to enhance the overall service experience. Customers felt heard, valued, and appreciated, increasing their likelihood of recommending the program to others, knowing their feedback contributes to meaningful improvements. For the service delivery team, the opportunity to voice concerns was a relief, fostering a culture of continuous improvement. Identifying pain points allowed them to address key challenges and focus on what truly matters. Ultimately, understanding the sources of dissatisfaction among both customers and service teams presents strategic and tactical opportunities to enhance service delivery and improve overall satisfaction.

Work Experience Program Introduction A work experience program essentially bridges the gap between theoretical knowledge and professional application; employers are increasingly seeking employees with hands-on-skills.  adjusting for market demands, many individuals, especially young professionals and those transitioning careers, are actively seeking work experience programs to enhance their resumes and gain practical skills. Problem Statement Whilst the demand for Work Experience Program theoretically exists, at ITYData, the reverse has been the case, demand for the Work Experience Program has waned. Analysis of our competitors show a transition from offering Work Experience Programs to Certification Programs. Clearly, the gap between theoretical knowledge and professional application does need bridging, however, is Work Experience Program still the best way to bridge that gap? Method  To analyse our Work Experience Program, holistically, identifying sources of dissatisfaction within the system and then addresses those issues, rather than focusing on individual components in isolation; the STATIK Kanban was applied. In analysing the sources of dissatisfaction associated with the Work Experience Program, an internal and external analysis was undertaken, the results are detailed in the following subsections Results  Result - Sociodemographic Patrons of the Work Experience Programs on average had the following sociodemographic characteristics: Transitioning into tech or agile  Less than 2 year of work experience  Between the age of 25 to 45 years  Had at least a bachelor's degree  Primarily living or in the process of emigrating to the UK, Canada or the USA Result - Purpose of the Work Experience Program Our respondents undertook our Work Experience Program for the following reasons: Seeking to subject knowledge in the space of tech or agile Seeking real life (project based) work experience   Seeking UK work experience reference  Result - Sources of Dissatisfaction with the Work Experience Program External  Patrons of the Work Experience Programs pointed the following issues with the Work Experience Program: An inability to offer real life (project based) work experience   Ambiguity associated with the validity, length and reliability of the UK work experience reference being offered  An unstructured and poorly delivered program with no clear learning objectives and expectation; material access was limited to video recordings  Limited operational capacity  Internal Conveyors of the Work Experience Programs pointed the following issues with the Work Experience Program: Limited operational capacity An unstructured and poorly delivered program with no clear learning objectives and expectations No access to external real life (project based) work experience projects Inability to utilise internal projects to mimic real life (project based) work experience projects Lack of evaluation measures Conclusion The work experience business can be quite profitable, especially when targeting specific demographics like students, career changers, or individuals seeking to gain skills in a niche market, as there is a high demand for practical experience and employers are increasingly valuing hands-on knowledge. The low patronage and the evolution of competitors from the Work Experience Programs can be primarily attributed to the inability to create real-life experiences coupled with a lack of supportive ecosystem.  Recommendations Clearly the work experience program is valuable, albeit when undertaken rightly. Key success factors for a work experience program include clear program structure and goals, effective communication, strong mentorship, relevant tasks and challenges, performance feedback, opportunities for skill development, career guidance, positive employer relationships, and a structured evaluation process to measure the program's impact on participants and identify areas for improvement

Product Owner in A Scaled Agile Framework Business people and developers must work together daily throughout the project - Agile Manifesto Summary A Product Owner in SAFe: Primary advocate of the customers Champions strategy for an agile team Part of the larger Product Management function Maintains alignment with the Solution Vision throughout development Aligns the Agile Team's effort with the organisation's strategic goals from the lens of the customers and stakeholders Manages the needs of customers and stakeholders Guides the evolution of the Solution to deliver maximum value Requires vision, excellent communication skills, and exception decision-making Who is a Product Owner in Scaled Agile Framework? Product Owner in Scaled Agile Framework (SAFe) is the Agile team member (SAFe Scrum Team / SAFe Kanban Team) responsible for maximising the value delivered by the team by ensuring that the team backlog is aligned with customer and stakeholder needs. What are the roles and responsibilities of a Product Owner in Scaled Agile Framework? A SAFe Product Owner acts as the 'voice of the customer' within the Agile Team. Thus, representing the needs of the customers, users, stakeholders and business. A successful SAFe Product Owner is proficient in managing relationships, synthesising data, maintaining business alignment in the team backlog, and communicating with various stakeholders. A key goal of a SAFe Product Owner is to obtain insights and delivery results quickly. In collaboration with the Product Management function, a SAFe Product Owner ensure that product strategy and implementation align within an agile team and across agile teams. The role of a SAFe Product Owner is critical in an organisation moving or working in Agile Ways of Working as a SAFe Product Owner acts as a bridge between an Agile Team and its customers. A SAFe Product Owner represent multiple, diverse and differing views of the customers and decided on what the most important work to complete inorder to maximum value and achieve business goals. A SAFe Product Owner acts in order to better business outcomes by managing the team backlog, gather feedback, promote teamwork, communicate effectively, and keep the team focused on the highest-value work. The key responsibilities of a SAFe Product Owner includes: Maximising value of the Agile Team Contributing to the product vision and product roadmap Define and prioritise the team backlog Ensure alignment with business goals Collaborate with product management Refine business requirements and acceptance criteria Participate in PI (Program Increment) planning Engage with agile teams daily Support continuous delivery and deployment Work with stakeholders for feedback Drive Innovation and Customer Value Interested in gaining work experience as a Product Owner, Check out Product Owner Work Experience Program - https://www.itydata.com/work-experience/-product-owner-work-experience-program

Background This is a series of posts for Project Brief - Sales Process Transformation using STATIK It is an evolutionary and collaborative approach to implementing Kanban for ITyDATA Sales Team. It helps understand: Purpose of ITyDATA Sales System as a Customer Understand sources of dissatisfactions with ITyDATA Sales System - customers and service delivery team Analyse work demand for ITyDATA Sales System Analyse current delivery capability for ITyDATA Sales System Model current system Workflow for ITyDATA Sales System Identify Classes of Service for ITyDATA Sales System Define Kanban System for ITyDATA Sales System Socialise and Negotiate expectations for ITyDATA Sales System Aim Understanding sources of dissatisfactions with ITyDATA Sales System Sources of Dissatisfaction - Customers Sources of Dissatisfaction - Service Delivery Team Sources of Dissatisfaction - Top Priorities Method Three 1 hour workshops using Miro were conducted to understand sources of dissatisfactions with ITyDATA Sales System - customers, service delivery team, and top priorities Workshop 1 - Understand sources of dissatisfactions from customer viewpoints Workshop 2 - Understand sources of dissatisfactions from service delivery team viewpoints Workshop 3 - Identifying top priorities of dissatisfactions - 1 customer and 1 service delivery team Results - Understanding Sources of Dissatisfaction Understand sources of dissatisfactions from customer viewpoints Example of customers' dissatisfactions are: Cant buy my work experience programme online No payment link on website Enroll button is not working No testimonal No example of previous work experience Understanding sources of dissatisfaction - Customer Understand sources of dissatisfactions from service delivery team viewpoints Example of service delivery teams' dissatisfactions are: Sales is not automated No dedicated sales person / team No sales infrastructure Too much work Marketing is not feeding into sales Understanding sources of dissatisfaction - Service Delivery Team Sources of Dissatisfaction - Top Priorities Identifying top priorities of dissatisfactions are: Customer - Cant buy my work experience programme online Service Delivery Team - Sales is not automated Top Priorities - Customer and Service Delivery Team Conclusion Significant dissatisfaction amongst customer towards ITyDATA Sales System Significant dissatisfaction amongst team member towards ITyDATA Sales System Top areas to focus on are Buying courses online and automating sales system Recommendation Action top areas of focus - Buying courses online and automating sales system using a Kanban System and evolutionary mindset Complete step 3 - 8 to realise and experience truly transformation of ITyDATA Sales System Key next step - Analyse demand for ITyDATA Sales System Where new work comes from? What is the arrival rate of new requests? What are your customer's expectations?

Conference Talk Review Conference Talk Review Criteria 1 - Conflict of Interest Criteria 1 - Description Conference Talk Review - If you have a conflict of interest, please declare it here. A conflict of interest may include, but is not limited to: having a personal relationship with a submitter, having a business relationship with a submitter, being in direct competition with the submitter and/or their business, or having any other relationship that may bias your judgment. If you’re unsure whether there’s a conflict of interest, please skip this review and/or check with your sub-committee lead for guidance. Criteria 1 - Option Option Outcome Yes Skip Review No Continue with review Conference Talk Review Criteria 2 - Session Title Criteria 2 - Description Conference Talk Review - Is the session title catchy, does it generate interest, and is it aligned with the topic? Or, does the session title need to be reworked to better align with the session topic or to generate interest? Criteria 2 - Option Option Score Yes - the title is catchy, generates interest, and is aligned with topic 1 No - the title needs to be reworked 0 Conference Talk Review Criteria 3 - What is the session about? Criteria 3 - Description Conference Talk Review - By referring to the information provided in the Session Summary section, it is evident that the session topic is relevant, insightful and/or interesting. Please indicate whether the topic is relevant, interesting, and valuable to the target audience. Criteria 3 - Option Option Score Definitely Is - the session topic is definitely relevant, insightful and /or interesting 3 Probably Is - the session topic is probably relevant, insightful and /or interesting 2 Probably Is Not - the session topic is probably not relevant, insightful and /or interesting 1 Definitely Is Not - the session topic is definitely not relevant, insightful and /or interesting 0 Conference Talk Review Criteria 4 - Learning - What will attendees take away from the session? Criteria 4 - Description Conference Talk Review - By referring to the information provided in the Learning Outcomes, Session Delivery, and Session Time Block sections, the session has at least three clear learning outcomes that will provide attendees with new knowledge and/or skills that they will be able to apply to their work and/or life. Please indicate whether attendees will gain new knowledge and/or skills that can be applied to their work or life after attending the session. Criteria 4 - Option Option Score Definitely Will - attendees will definitely gain applicable knowledge and/or skills 3 Probably Will - attendees will probably gain applicable knowledge and/or skills 2 Probably Will Not - attendees will probably not gain applicable knowledge and/or skills 1 Definitely Will Not - attendees will definitely not gain applicable knowledge and/or skills 0 Conference Talk Review Criteria 5 - Format - How is the session delivered? Criteria 5 - Description Conference Talk Review - By referring to the information provided in the Learning Outcomes, Session Delivery, and Session Time Block sections, the organization of the session is clear. The author has included a session outline and has identified key activities and/or resources to be used during the session. Please indicate whether the session format will support the learning outcomes. Criteria 5 - Option Option Score Definitely Will - the session format will definitely hold attendees' interest and achieve the learning outcomes 3 Probably Will - the session format will probably hold attendees' interest and achieve the learning outcomes 2 Probably Will Not - the session format will probably not hold attendees' interest and achieve the learning outcomes 1 Definitely Will Not - the session format will definitely not hold attendees' interest and achieve the learning outcomes 0 Conference Talk Review Criteria 6 - Final Evaluation Criteria 6 - Description Conference Talk Review - Given everything you’ve read for this submission, would you accept this session into the program? Criteria 6 - Option Option Score Yes, accept this session as it will be valuable for the program and a worthwhile investment 3 No, unless there are revisions made based on the provided feedback 0 No, do not accept the session 0 Conference Talk Review Criteria 7 - Elaborate on Your Final Evaluation Response Criteria 7 - Description Conference Talk Review - Please provide your most significant argument(s) for ACCEPTING or NOT ACCEPTING this submission? Include any additional feedback you have for the submission Criteria 7 - Option Free text section with no option Conference Talk Review Criteria 8 - Feedback for the Speaker Criteria 8 - Description Conference Talk Review - Please provide clear and actionable feedback about the submission that can be shared with the speaker. Please note, this feedback will be sent to the speaker as-is, so please use discretion and thoughtfulness when generating your feedback. Criteria 8 - Option Free text section with no option

Our vision is for every customer to have an awesome sales experience Project Summary Duration - November 24 - March 25 Category - Internal Project Client - ITyDATA Sales Team Delivery / Collaborating Team(s) - ITyDATA Customer Experience Team,  ITyDATA Business Transformation Team, ITyDATA Data Team, and ITyDATA Technology Team Background At ITyData, we envision an awesome sales experience for all customers be it a prospective customer or a life long customer. Every customers deserve to an incredible sales experience at ITyData. In order to achieve our vision, we are embarking on Programme - Sales Experience Transformation. The first project within this programme is Project - Sales Process Transformation Key Objective For Sales Process Transformation using STATIK The key objective of this project is to apply STATIK method to transform our current sales process Key Results For Sales Process Transformation using STATIK ID Key Result Key Outcome Key Result 1 Presentation on Purpose of ITyDATA Sales System A clear understanding of the purpose of ITyDATA Sales System Key Result 2 Presentation on Sources of Dissatisfication of ITyDATA Sales System A deeper understanding of the different source of dissatisfaction, challenges and difficulties with ITyDATA sales process system Key Result 3 Presentation on Demand analysis of ITyDATA Sales System A deeper and factual understanding of the current demand and expected demand of ITyDATA Sales System Key Result 4 Presentation on Capability Analysis of ITyDATA Sales System A deeper and factual understanding of the current capability and expected capability of ITyDATA Sales System Key Result 5 Model current ITyDATA sales process using Gliffy A clear representation of our current sales process - As Is Process Flow Key Result 6 Presentation on Classes of Services for ITyDATA sales system A clear understanding and identification of different types of classes of services and their properties / behaviours / delivery expectation Key Result 7 Design a Kanban System for ITyDATA Sales System in Jira Beginning an evoluntionary transforming using Kanban for ITyDATA sales team Key Result 8 Socialise the Kanban System - 1 Presentation, 1 blog and 1 public workshop Showcase the evoluntionary transformation of ITyDATA sales system using STATIK Useful Links STATIK video series - https://youtube.com/playlist?list=PL1oHUv7_nqgN276sz45Xup_ryMTW7Slxh&si=r_g_osXWass23TW5 STATIK: Introducing Kanban the Right Way - https://businessmap.io/blog/statik-kanban STATIK – The Most Effective Way to Get Started with Kanban - https://allthingsagile.co/post/statik/ STATIK (Systems Thinking Approach to Introduce Kanban) - https://hjavixcs.medium.com/statik-systems-thinking-approach-to-introduce-kanban-13996dbe414a

Dear Candidate, Congratulations on face to face interview with Zenix Investment. I would suggest you dress suit and tie for interview, as a business they are business casual. Upon arrival please ask for Femi Martins or Daniel Rotimi. I would suggest arriving 15 mins early as they have a busy reception desk and take some photo ID, in case its asked (drivers licence / passport) For this stage, you will be asked to prepare and deliver a brief presentation covering the following topics (max 10 mins, 4 slides): A portfolio manager has asked us to include a new feature into our quantitative commodity tracker that you are a business analyst on, related to our quantitative sustainable investment platform. As a business analyst it is critical that you understand the requirements of a user and their expectations Please can you: Explain commodity investing What are some signals that a portfolio manager will be interested in? Give a brief explanation of these. Describe how a new function using AI could help a portfolio manager with quantitative sustainable investment management How does quantitative modelling affect investment performance? How will you describe this to a developer with no investment knowledge Please can you email the presentation before the interview to Femi and Daniel so I can present on the screen The purpose of this presentation is to see the style and approach the candidate will take and to see how they think about users. Interview Date - Next Monday Interview Time - 12:00 (60 minutes) Location : 2rd Floor, One Canada Square, Canary Wharf, UK Meeting with : Femi Martins - Hiring Manager - Investment Programme Manager Daniel Rotimi - Senior Business Analyst Xi Wan - Investment Technical Lead This stage will be a 10 minute presentation followed by Q&A and discussions around the presentation and your experience and skills around Business Analysis